Bibliographic record
Abstract
Christina Wolleon, BS, RN, MSN, APN-BC, is an Adult/Geriatric Nurse Practitioner currently employed at Holy Name Medical Center in Teaneck, New Jersey, working in outpatient primary care as well as inpatient and nursing home settings. Before obtaining her MSN degree from Saint Peter's University in 2012, she worked as a registered nurse, with experience in medical, surgical, and emergency department settings. With her Bachelor of Science (BSN) degree in biology from The College of New Jersey and Diploma and Master's degrees in nursing, she is currently in pursuit of her Doctor of Nursing Practice (DNP) degree at Saint Peter's University. Address correspondence to Lynn S. Muller, Esq., Muller & Muller, 15 West Main Street, Suite C, PO Box 164, Bergenfield, NJ 07621. If you have an idea you would like to discuss, send your contact information by e-mail and you will contacted by your preferred method. Disclaimer: The information contained in this department is for educational purposes only. It is not legal advice, which can only be given by an attorney admitted to practice in the jurisdiction/state(s) in which you practice. Do you have a question or issue you would like addressed here? Questions are always welcome. We encourage ALL readers to submit questions and/or manuscripts, as well as topics you would like to see addressed in this department. Questions and other inquiries are accepted by e-mail at: [email protected] The authors report no conflicts of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".